AI RAG definition

AI RAG definition refers to Retrieval-Augmented Generation, a foundational artificial intelligence technique that enhances language models by combining external information retrieval with text generation capabilities. RAG systems address critical AI limitations including hallucinations, knowledge gaps, and outdated information by retrieving relevant context from external databases before generating responses. The process involves converting user queries into vector embeddings, performing semantic searches across indexed knowledge repositories, and augmenting language model prompts with retrieved information to produce grounded, factual outputs. Core components include embedding models for semantic understanding, vector databases for efficient storage and retrieval, chunking strategies for optimal document segmentation, and ranking algorithms for relevance scoring. AI RAG implementations enable systems to access real-time data, proprietary knowledge bases, and domain-specific information while preserving the natural language capabilities of foundation models. This architecture has become essential for enterprise AI applications, particularly agentic AI systems requiring accurate, current information for autonomous decision-making and complex workflow execution.